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International Journal of Photoenergy
Volume 2016 (2016), Article ID 8506193, 16 pages
Research Article

Solar Energy Validation for Strategic Investment Planning via Comparative Data Mining Methods: An Expanded Example within the Cities of Turkey

1Faculty of Engineering and Architecture, Industrial Engineering, Cukurova University, 01330 Adana, Turkey
2Faculty of Arts, IRIO Department, Groningen University, 9712 EK Groningen, Netherlands

Received 30 January 2016; Revised 18 April 2016; Accepted 4 May 2016

Academic Editor: Alessandro Burgio

Copyright © 2016 Oya H. Yuregir and Cagri Sagiroglu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Energy supply together with the data management is one of the key challenges of our century. Specifically, to decrease the climate change effects as energy requirement increases day by day poses a serious dilemma. It can be adequately reconciled with innovative data management in (renewable) energy technologies. The new environmental-friendly planning methods and investments that are discussed by researchers, governments, NGOs, and companies will give the basic and most important variables in shaping the future. We use modern data mining methods (SOM and -Means) and official governmental statistics for clustering cities according to their consumption similarities, the level of welfare, and growth rate and compare them with their potential of renewable resources with the help of Rapid Miner 5.1 and MATLAB software. The data mining was chosen to make the possible secret relations visible within the variables that can be unpredictable at first sight. Here, we aim to see the success level of the chosen algorithms in validation process simultaneously with the utilized software. Additionally, we aim to improve innovative approach for decision-makers and stakeholders about which renewable resource is the most suitable for an exact region by taking care of different variables at the same time.